Highly integrated annotation and segmentation system for medical imaging

ABSTRACT

A method for training a segmentation correction model includes performing an iterative model training process over a plurality of iterations. During each iteration, an initial segmentation estimate for an image is provided to a human annotators via an annotation interface. The initial segmentation estimate identifies one or more anatomical areas of interest within the image. Interactions with the annotation interface are automatically monitored to record annotation information comprising one or more of (i) segmentation corrections made to the initial segmentation estimate by the annotators via the annotation interface, and (ii) interactions with the annotation interface performed by the annotators while making the corrections. A base segmentation machine learning model is trained to automatically create a base segmentation based on the image. Additionally, a segmentation correction machine learning model is trained to automatically perform the segmentation corrections based on the image.

TECHNOLOGY FIELD

The present invention relates generally to methods and systems forautomating the annotation and segmentation of medical image data. Thetechniques described herein may generally be applied to any medicalimaging modality including, without limitation, Magnetic Resonance (MR),Ultrasound, and Computed Tomography (CT) images.

BACKGROUND

Automation of medical imaging requires algorithms to learn how toperform a particular task, and these algorithms require “ground truth”data for training and validation. This ground truth data comes fromhuman experts annotating the data, but such annotations aretime-consuming and expensive to obtain. Key problems include how toobtain annotation data efficiently, with minimal effort from the humanexperts, and how to obtain the right amount of labeled data withoutpaying for more than is actually needed. For machine learning algorithmsan additional challenge is knowing when a sufficiently accurate resulthas been achieved. Finally, the entire cycle of annotation, testing, andvalidation is slow, limiting the overall pace of innovation.

There have been many machine algorithms trained with data annotated byhuman experts. In a typical development cycle, researchers guess howmuch training data will be needed and then employ human experts toprovide it. Prior research focused on how best to train given a set ofannotated data.

Recently, Deep Learning has emerged as a popular and highly effectivemethod for performing image segmentation. A segmentation of an image isproduced by portioning an image into different segments. For medicalimages, these segments may correspond to biologically relevantstructures such as organs, blood vessels, pathologies, etc. However oneof the biggest limitations of Deep Learning is that large amounts oflabeled data are necessary to get good results without overfitting.

Medical images are difficult to annotate compared to ordinaryphotographs and videos. For example, different image modalities mayintroduce artifacts that are not readily identifiable by one withoutmedical training. Moreover, reliable detection of organs and otherrelevant anatomical structures, as well as identification of relevantdiseases and abnormalities, will be difficult, if not impossible unlessthe annotator has medical training. This makes medical image annotationmore costly to obtain as the number of people able to perform this taskis limited.

Current practices involve a sequential approach of first obtaining theannotations followed by algorithm development. Any benefits fromcreating the algorithm do not enhance the annotation acquisition. Inthis disclosure, we describe how the twin needs for segmentationalgorithm development and segmentation training data can be combinedinto a single process for a more efficient development cycle.Improvements in the algorithm development will speed up the annotation,whereas at the same time the actions of the annotators are used tosynchronously drive the learning algorithm.

SUMMARY

Embodiments of the present invention address and overcome one or more ofthe above shortcomings and drawbacks, by providing an integrated systemof manual annotation and automatic segmentation for medical imagingtasks. The techniques described herein build upon machine learningtechniques previously applied to object classification and semanticlabeling problems to automate the segmentation correction process. Thetechniques described herein offer improvements to variouscomputer-related technologies. For example, the disclosed techniquesusing computing systems to enable the automation of specific imageannotation and segmentation tasks that previously could not beautomated.

According to some embodiments, a method for training a segmentationcorrection model includes performing an iterative model training processover a plurality of iterations. During each iteration, an initialsegmentation estimate for an image is provided to a human annotators viaan annotation interface. The initial segmentation estimate identifiesone or more anatomical areas of interest within the image. Interactionswith the annotation interface are automatically monitored to recordannotation information comprising one or more of (i) segmentationcorrections made to the initial segmentation estimate by the annotatorsvia the annotation interface, and (ii) interactions with the annotationinterface performed by the annotators while making the corrections. Abase segmentation machine learning model is trained to automaticallycreate a base segmentation based on the image. Additionally, asegmentation correction machine learning model is trained toautomatically perform the segmentation corrections based on the image.

In some embodiments of the aforementioned method, the annotationinformation further comprises an effort measurement indicative of anamount of effort expended by the annotators in making the corrections.This effort measurement can be used to determine when to terminate thetraining process. For example, if the effort measurement is equal to theconvergence value, the iterative model training process may beterminated. Conversely, if the effort measurement is not equal to theconvergence value, the base segmentation and segmentation correctionmachine learning models may be used to determine the initialsegmentation estimate for a new image. Then, the iterative modeltraining process can continue to the next iteration.

In some embodiments of the aforementioned method, the effort measurementis a time-based measurement and the convergence value is equal to apredetermined time value. In other embodiments, the effort measurementis a measurement of time spent by the annotators in making thecorrections and number of interface motions made in making thecorrections. In one embodiment, the image comprises a plurality ofslices/volumes and the effort measurement includes a measurement of timespent in scrolling through the plurality of slices/volumes. In anotherembodiment, the effort measurement is a measurement of a number of mousemotions and the convergence value is equal to a predetermined number ofmouse motions. The effort measurement can also be used in modeltraining. For example, if the segmentation correction machine learningmodel is a convolutional neural network may be used to set one or moretraining weights used by the convolutional neural network.

According to another aspect of the present invention, a method fortraining a landmark location correction model includes performing aniterative model training process in a manner similar to the othermethods discussed above. However, rather than relying on an initialsegmentation estimate, initial landmark location estimates are providedfor an image to a plurality of human annotators via an annotationinterface. Each initial landmark location estimate identifies ananatomical landmark within the image.

According to other embodiments, a system for training a segmentationcorrection model includes an annotation system and a parallel computingplatform. The annotation system is configured to provide an initialsegmentation estimate for an image to a plurality of human annotatorsvia an annotation interface. The initial segmentation estimateidentifies one or more anatomical areas of interest within the image.The annotation system also automatically monitors interactions with theannotation interface to record annotation information comprising (i)segmentation corrections made to the initial segmentation estimate bythe annotators via the annotation interface, and (ii) interactions withthe annotation interface performed by the annotators while making thecorrections. The parallel computing platform is configured to train abase segmentation machine learning model to automatically create a basesegmentation based on the image. Additionally, the platform trains asegmentation correction machine learning model to automatically performthe segmentation corrections based on the image.

Additional features and advantages of the invention will be madeapparent from the following detailed description of illustrativeembodiments that proceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other aspects of the present invention are bestunderstood from the following detailed description when read inconnection with the accompanying drawings. For the purpose ofillustrating the invention, there is shown in the drawings embodimentsthat are presently preferred, it being understood, however, that theinvention is not limited to the specific instrumentalities disclosed.Included in the drawings are the following figures:

FIG. 1 is a view of a system for automating image segmentation,according to some embodiments;

FIG. 2 shows an example interface used by an annotation system,according to some embodiments;

FIG. 3 illustrates an example of the overall annotation/segmentationprocess, according to some embodiments; and

FIG. 4 provides an example of a parallel processing memory architecturethat may be utilized to implement one or more components shown in FIG.1, according to some embodiments of the present invention.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The following disclosure describes the present invention according toseveral embodiments directed at methods, systems, and apparatusesrelated to automated manual annotation and automatic segmentation formedical imaging tasks. Briefly, an annotation system is used to collectan initial segmentation estimate. This initial segmentation estimate isthen presented to one or more human annotators which make corrections tothe initial segmentation estimate. Based on the corrections made by theannotators, and interactions made the annotators in making thecorrections, a correction model and a segmentation model are trained. Bylearning the corrective actions taken by annotators to refine asegmentation result, the system described herein actively improves thesegmentation model, which differs from training the model withcumulative annotated data.

FIG. 1 is a view of a system for automating image segmentation,according to some embodiments. An Initial Segmentation Estimate 110 isprovided to Annotators 105 via an Annotation System 115. In someembodiments, this Initial Segmentation Estimate 110 is generated usingan already developed segmentation method that needs improvement. Forexample, one or more conventional techniques generally known in the artmay be used to provide a fast, but inaccurate segmentation which can beused as the Initial Segmentation Estimate 110. In other embodiments, theInitial Segmentation Estimate 110 can come from a simple ellipse placedby the user in the location of the object to be annotated. In yetanother alternative, the initial segmentation estimate can be defined bylabels (or “seeds”) placed by the user. For example, the user may labelcertain pixels as “object” or “background.” Once the labels have beenprovided, a generic seed-based algorithm such as the random walkeralgorithm may be applied to perform the segmentation.

In general, the Annotation System 115 includes a system for presenting agraphical user interface (GUI) which allows the user to performInteractions 112 (e.g., via mouse movements) which correct the InitialSegmentation Estimate 110. This GUI is referred to herein as an“annotation interface.” The computing technology supporting thisannotation interface can be implemented using any technique known in theart. For example, in some embodiments, the Annotation System 115 isinstalled as software on computing devices used by the Annotators 105.General purpose computing devices can be used in these embodiments, orspecialized devices with additional software or hardware to facilitateimage annotation. In other embodiments, the Annotation System 115 can becloud based. Thus, the Annotators 105 interact through a browser or someother thin client interface to interact with the annotation interface.FIG. 2 provides an example Interface 200 that may be employed by theAnnotation System 115. Here, an upper body anatomy is presented in aplurality of views and the user is presented with a variety of tools(located on the left hand side of the Interface 200) for adjusting theinitial segmentation (indicated by segmentation lines 210A, 210B, 210C,and 210D in FIG. 2).

Based on the Interactions 112 shown in FIG. 1, the Annotation System 115stores Corrections 120 that describe the refinement of the segmentationfrom initial estimate to ground truth. These Corrections 120 indicateone or more changes to the Initial Segmentation Estimate 110. Forexample, in some embodiments, the Initial Segmentation Estimate 110 ispresented as a series of points around the outer edge of the anatomy tobe segmented. The Annotators 105 may adjust the Initial SegmentationEstimate 110 by clicking and dragging these points until the boundary ofthe segmentation accurately reflects the edge of the anatomical area ofinterest. In this case the Corrections 120 could comprise thecoordinates of the points in the new segmentation or adjustments to thepoints in the Initial Segmentation Estimate 110. In other embodiments,rather than storing the Corrections 120 as points, just the shape of thenew segmentation may be included in the Corrections 120.

As the annotators perform the Interactions 112 with the AnnotationSystem 115, the annotation system records their Motions 125 to adjustthe initial estimate. Broadly speaking, the corrections may be thoughtof moving the contour inward in the case of over-segmentation, or movingthe contour outward in the case of under-segmentation. These inward oroutward motions, along with the places where they are performed, serveas input to a classifier, as described below. In addition to the Motions125, the annotation system may record an Effort Measurement 130 whichindicates the amount of effort expended by the annotators to perform thecorrections. Effectively, the Effort Measurement 130 provides a measureof how close the initial result was to “perfect.” Amount of effort mayinclude, for example, overall time, number of mouse motions, amount ofscrolling through slices for multi-slice images, etc. The effortmeasurement may be used, for example, to give larger weights to suchcases during training, and to determine whether the overall system hasconverged.

It should be noted that the approach described above is not limited tosegmentation of objects, but may also be used in other applications suchas landmark or object detection. In these applications, the input is aninitial guess of the landmark location, and the actions of theannotators to move the location to the correct location are recorded andused in the machine learning model described below. In addition, theamount of effort required may be recorded.

The Initial Segmentation Estimate 110 is combined with the Interactions112, the Motions 125, and the Effort Measurement 130 to form anAnnotated Correction 135 which is presented to a Modeling Computer 140.In some embodiments, the Annotated Correction 135 further includes theimage data which is being analyzed. In other embodiments, the AnnotatedCorrection 135 only includes an identifier (e.g., filename) of the imagedata which can then be used to retrieve the image data from theAnnotation System 115, either locally at the Modeling Computer 140 or onanother system (not shown in FIG. 1).

The Modeling Computer 140 is assumed to be connected to the AnnotationSystem 115 via one or more networks (e.g., the Internet) not shown inFIG. 1; however, in some embodiments, the Modeling Computer 140 and theAnnotation System 115 can be combined in a single computing system. Thedata included in the Annotated Correction 135 may be transmitted to theModeling Computer 140 using any technique known in the art. For example,in one embodiment, the Annotated Correction 135 comprise a data filecontaining the Initial Segmentation Estimate 110 and a second data filedescribing the Interactions 112, the Motions 125, and the EffortMeasurement 130 in a structured data language such as Extensible MarkupLanguage (XML).

A Segmentation Model 147 (i.e., classifier) is also learned from theAnnotated Correction 135. More specifically, the Segmentation Model 147is trained to perform the segmentation provided in the SegmentationModel 147 when presented with corresponding image data. Thus, oncetrained the Segmentation Model 147 is capable of automaticallysegmenting an image without any need for manual annotation. The accuracyof the segmentation will be dependent on the level of training providedto the Segmentation Model 147. In some embodiments, the SegmentationModel 147 simply outputs a segmentation, but additional information mayalso be provided such as the accuracy of the segmentation (based onmodeling results). Furthermore, in some embodiments, the SegmentationModel 147 may suggest more than one segmentation based on modelingresults and a clinician can select the preferred segmentation based onmanual inspection of the data.

The Modeling Computer 140 includes a Ground Truth Database 145 whichstores the ground truth for each image presented to the Annotators 105for segmentation. A Correction Model 150 (i.e., classifier) is learnedfrom the difference between the Initial Segmentation Estimate 110 andthe Annotated Correction 135 using a machine learning algorithm. TheEffort Measurement 130 included in the Annotated Correction 135 is usedto adjust the training weights so that the learning evolves faster whenthe estimate is far from the ground truth, and slows down when theestimate is close to the ground truth. Note that the learning step mayoccur after a certain amount of annotations have been performed orimmediately after each annotation.

The Segmentation Model 147 and the Correction Model 150 may generally beany classifier known in the art. In one embodiment, the SegmentationModel 147 and the Correction Model 150 are organized as a recursiveconvolutional neural network. In another embodiment, the SegmentationModel 147 and the Correction Model 150 are organized as a generativeadversarial neural network. Combinations of recursive convolutional andgenerative adversarial neural networks can also be used as well as otherdeep learning architectures.

When applied to an image, the Segmentation Model 147 generates asegmentation, referred to herein as the “base segmentation,” for theimage. The output of the Correction Model 147, when applied to theimage, is referred to herein as the “segmentation correction” for theimage. The base segmentation and the segmentation correction arecombined as the Updated Segmentation Estimate 155 and input into theAnnotation System 115. This Updated Segmentation Estimation 155 isprovided to the Annotators 105 via the Annotation System 115. In thisway, the work load on the Annotators 105 is systematically reduced asthe Segmentation Model 147 and the Correction Model 150 become better atautomating segmentation. The process of presenting a segmentation andusing annotated corrections to train the models may be repeated untilthe system converges at which point training is complete.

As an example implementation of the techniques described above withreference to FIG. 1, consider a task of 3D liver segmentation on CT. TheModeling Computer 140 can use a fully convolutional neural network tolearn the base segmentation and the correction provided by theannotators. The system continues to update both the segmentation andcorrection models along with new incoming annotations until the effortof correction by the annotators is close to or at zero, or meets theaccuracy required by the task.

By taking this approach, the system collects only as much training dataas is needed to achieve the goal. In contrast to traditional learningapproaches, where researchers must guess how much training data isrequired by a task, the method described above more efficiently limitsthe amount of data collected to what is needed. As a result, the amountof annotation resources required to support algorithm development can bereduced. Moreover by continuously incorporating improvements in thealgorithm from what is learned by earlier corrections, the efforts ofthe annotators are continuously reduced on subsequent passes. Since mostannotators are paid by the hour, this will result in a considerablesaving in the overall cost of developing new algorithms.

FIG. 3 illustrates an example of the overall annotation/segmentationprocess, with the actions of the annotator on top and the actions of thesegmentation algorithm on bottom. The images depict an organ to besegmented. The segmentation result for an organ is shown as cross-hatchpattern, the white outlined clear sections indicate the areas ofover-segmentation, and white filled sections indicate the areas ofunder-segmentation. The actions of the annotators to correct these areasare used as inputs to the training algorithm. The amount of effortrequired to make the correction is used to adjust the training weightsand detect convergence.

FIG. 4 provides an example of a parallel computing platform 400 that maybe utilized to implement the modeling computer 140 shown FIG. 1,according to some embodiments of the present invention. This platform400 may be used in embodiments of the present invention where NVIDIACUDA™ (or a similar parallel computing platform) is used. Thearchitecture includes a host computing unit (“host”) 405 and a graphicsprocessing unit (GPU) device (“device”) 410 connected via a bus 415(e.g., a PCIe bus). The host 405 includes the central processing unit,or “CPU” (not shown in FIG. 4), and host memory 425 accessible to theCPU. The device 410 includes the graphics processing unit (GPU) and itsassociated memory 420, referred to herein as device memory. The devicememory 420 may include various types of memory, each optimized fordifferent memory usages. For example, in some embodiments, the devicememory includes global memory, constant memory, and texture memory.

Parallel portions of a big data platform and/or big simulation platform(see FIG. 4) may be executed on the platform 400 as “device kernels” orsimply “kernels.” A kernel comprises parameterized code configured toperform a particular function. The parallel computing platform isconfigured to execute these kernels in an optimal manner across theplatform 400 based on parameters, settings, and other selectionsprovided by the user. Additionally, in some embodiments, the parallelcomputing platform may include additional functionality to allow forautomatic processing of kernels in an optimal manner with minimal inputprovided by the user.

The processing required for each kernel is performed by grid of threadblocks (described in greater detail below). Using concurrent kernelexecution, streams, and synchronization with lightweight events, theplatform 400 of FIG. 4 (or similar architectures) may be used toparallelize portions of the model based operations performed in trainingthe Correction Model 150 or the Segmentation Model 147 shown in FIG. 1

The device 410 includes one or more thread blocks 430 which representthe computation unit of the device 410. The term thread block refers toa group of threads that can cooperate via shared memory and synchronizetheir execution to coordinate memory accesses. For example, in FIG. 4,threads 440, 445 and 450 operate in thread block 430 and access sharedmemory 435. Depending on the parallel computing platform used, threadblocks may be organized in a grid structure. A computation or series ofcomputations may then be mapped onto this grid. For example, inembodiments utilizing CUDA, computations may be mapped on one-, two-, orthree-dimensional grids. Each grid contains multiple thread blocks, andeach thread block contains multiple threads. For example, in FIG. 4, thethread blocks 430 are organized in a two dimensional grid structure withm+1 rows and n+1 columns. Generally, threads in different thread blocksof the same grid cannot communicate or synchronize with each other.However, thread blocks in the same grid can run on the samemultiprocessor within the GPU at the same time. The number of threads ineach thread block may be limited by hardware or software constraints.

Continuing with reference to FIG. 4, registers 455, 460, and 465represent the fast memory available to thread block 430. Each registeris only accessible by a single thread. Thus, for example, register 455may only be accessed by thread 440. Conversely, shared memory isallocated per thread block, so all threads in the block have access tothe same shared memory. Thus, shared memory 435 is designed to beaccessed, in parallel, by each thread 440, 445, and 450 in thread block430. Threads can access data in shared memory 435 loaded from devicememory 420 by other threads within the same thread block (e.g., threadblock 430). The device memory 420 is accessed by all blocks of the gridand may be implemented using, for example, Dynamic Random-Access Memory(DRAM).

Each thread can have one or more levels of memory access. For example,in the platform 400 of FIG. 4, each thread may have three levels ofmemory access. First, each thread 440, 445, 450, can read and write toits corresponding registers 455, 460, and 465. Registers provide thefastest memory access to threads because there are no synchronizationissues and the register is generally located close to a multiprocessorexecuting the thread. Second, each thread 440, 445, 450 in thread block430, may read and write data to the shared memory 435 corresponding tothat block 430. Generally, the time required for a thread to accessshared memory exceeds that of register access due to the need tosynchronize access among all the threads in the thread block. However,like the registers in the thread block, the shared memory is typicallylocated close to the multiprocessor executing the threads. The thirdlevel of memory access allows all threads on the device 410 to readand/or write to the device memory. Device memory requires the longesttime to access because access must be synchronized across the threadblocks operating on the device. Thus, in some embodiments, theprocessing of each individual annotation in the Annotated Correction 135is coded such that it primarily utilizes registers and shared memory andonly utilizes device memory as necessary to move data in and out of athread block.

The embodiments of the present disclosure may be implemented with anycombination of hardware and software. For example, aside from parallelprocessing architecture presented in FIG. 4, standard computingplatforms (e.g., servers, desktop computer, etc.) may be speciallyconfigured to perform the techniques discussed herein. In addition, theembodiments of the present disclosure may be included in an article ofmanufacture (e.g., one or more computer program products) having, forexample, computer-readable, non-transitory media. The media may haveembodied therein computer readable program code for providing andfacilitating the mechanisms of the embodiments of the presentdisclosure. The article of manufacture can be included as part of acomputer system or sold separately.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

An executable application, as used herein, comprises code or machinereadable instructions for conditioning the processor to implementpredetermined functions, such as those of an operating system, a contextdata acquisition system or other information processing system, forexample, in response to user command or input. An executable procedureis a segment of code or machine readable instruction, sub-routine, orother distinct section of code or portion of an executable applicationfor performing one or more particular processes. These processes mayinclude receiving input data and/or parameters, performing operations onreceived input data and/or performing functions in response to receivedinput parameters, and providing resulting output data and/or parameters.

A graphical user interface (GUI), as used herein, comprises one or moredisplay images, generated by a display processor and enabling userinteraction with a processor or other device and associated dataacquisition and processing functions. The GUI also includes anexecutable procedure or executable application. The executable procedureor executable application conditions the display processor to generatesignals representing the GUI display images. These signals are suppliedto a display device which displays the image for viewing by the user.The processor, under control of an executable procedure or executableapplication, manipulates the GUI display images in response to signalsreceived from the input devices. In this way, the user may interact withthe display image using the input devices, enabling user interactionwith the processor or other device.

The functions and process steps herein may be performed automatically orwholly or partially in response to user command. An activity (includinga step) performed automatically is performed in response to one or moreexecutable instructions or device operation without user directinitiation of the activity.

The system and processes of the figures are not exclusive. Othersystems, processes and menus may be derived in accordance with theprinciples of the invention to accomplish the same objectives. Althoughthis invention has been described with reference to particularembodiments, it is to be understood that the embodiments and variationsshown and described herein are for illustration purposes only.Modifications to the current design may be implemented by those skilledin the art, without departing from the scope of the invention. Asdescribed herein, the various systems, subsystems, agents, managers andprocesses can be implemented using hardware components, softwarecomponents, and/or combinations thereof. No claim element herein is tobe construed under the provisions of 35 U.S.C. 112(f), unless theelement is expressly recited using the phrase “means for.”

We claim:
 1. A method for training a segmentation correction model, themethod comprising: performing an iterative model training process over aplurality of iterations, wherein each iteration comprises: providing aninitial segmentation estimate for an image to a plurality of humanannotators via an annotation interface, wherein the initial segmentationestimate identifies one or more anatomical areas of interest within theimage; automatically monitoring interactions with the annotationinterface to record annotation information comprising one or more of (i)segmentation corrections made to the initial segmentation estimate bythe annotators via the annotation interface, (ii) interactions with theannotation interface performed by the annotators while making thecorrections, and (iii) an effort measurement indicative of an amount ofeffort expended by the annotators in making the corrections; training abase segmentation machine learning model to automatically create a basesegmentation based on the image; and training a segmentation correctionmachine learning model to automatically perform the segmentationcorrections based on the image; if the effort measurement is equal to aconvergence value, terminating the iterative model training process; andif the effort measurement is not equal to the convergence value, usingthe base segmentation machine learning model and segmentation correctionmachine learning model to determine the initial segmentation estimatefor a new image and continuing to a next iteration of the iterativemodel training process; wherein the effort measurement is a measurementof a number of user-generated mouse motions with respect to theannotation interface, and the convergence value is equal to apredetermined number of user-generated mouse motions with respect to theannotation interface.
 2. The method of claim 1, wherein the effortmeasurement further comprises a time-based measurement and theconvergence value further comprises a predetermined time value.
 3. Themethod of claim 2, wherein the time-based measurement is a measurementof time spent by the annotators in making the corrections.
 4. The methodof claim 2, wherein the image comprises a plurality of slices/volumesand the effort measurement includes a measurement of time spent inscrolling through the plurality of slices/volumes.
 5. The method ofclaim 1, wherein the segmentation correction machine learning model is aconvolutional neural network.
 6. The method of claim 5, wherein theeffort measurement is used to set one or more training weights used bythe convolutional neural network.
 7. A method for training a landmarklocation correction model, the method comprising: performing aniterative model training process over a plurality of iterations, whereineach iteration comprises: providing initial landmark location estimatesfor an image to a plurality of human annotators via an annotationinterface, wherein each initial landmark location estimate identifies ananatomical landmark within the image; automatically monitoringinteractions with the annotation interface to record annotationinformation comprising (i) corrections to the initial landmark locationestimates made by the annotators via the annotation interface, (ii)interactions with the annotation interface performed by the annotatorswhile making the corrections, and (iii) an effort measurement indicativeof an amount of effort expended by the annotators in making thecorrections; training a landmark location machine learning model toautomatically identify landmark locations in the image based on theannotation information; training a location correction machine learningmodel to automatically perform the corrections to the initial landmarklocation estimates; if the effort measurement is equal to a convergencevalue, terminating the iterative model training process; and if theeffort measurement is not equal to the convergence value, using thelandmark location and the location correction machine learning model todetermine the initial landmark location estimate for a new image andcontinuing to a next iteration of the iterative model training process,wherein the effort measurement is a measurement of a number ofuser-generated mouse motions with respect to the annotation interface,and the convergence value is equal to a predetermined number ofuser-generated mouse motions with respect to the annotation interface.8. The method of claim 7, wherein the effort measurement furthercomprises a time-based measurement and the convergence value furthercomprises a predetermined time value.
 9. The method of claim 8, whereinthe time-based measurement is a measurement of time spent by theannotators in making the corrections.
 10. The method of claim 8, whereinthe image comprises a plurality of slices and the effort measurementfurther comprises a measurement of time spent in scrolling through theplurality of slices.
 11. The method of claim 7, wherein the locationcorrection machine learning model is a convolutional neural network. 12.The method of claim 11, wherein the effort measurement is used to setone or more training weights used by the convolutional neural network.13. A system for training a segmentation correction model by performingan iterative model training process over a plurality of iterations, thesystem comprising: an annotation system configured to: provide aninitial segmentation estimate for an image to a plurality of humanannotators via an annotation interface, wherein the initial segmentationestimate identifies one or more anatomical areas of interest within theimage; automatically monitor interactions with the annotation interfaceto record annotation information comprising (i) segmentation correctionsmade to the initial segmentation estimate by the annotators via theannotation interface, and (ii) interactions with the annotationinterface performed by the annotators while making the corrections, and(iii) an effort measurement indicative of an amount of effort expendedby the annotators in making the corrections; a parallel computingplatform configured to: train a base segmentation machine learning modelto automatically create a base segmentation based on the image; andtrain a segmentation correction machine learning model to automaticallyperform the segmentation corrections based on the image; if the effortmeasurement is equal to a convergence value, terminate the iterativemodel training process; and if the effort measurement is not equal tothe convergence value, use the base segmentation machine learning modeland segmentation correction machine learning model to determine theinitial segmentation estimate for a new image and continue to a nextiteration of the iterative model training process; wherein the effortmeasurement is a measurement of a number of user-generated mouse motionswith respect to the annotation interface, and the convergence value isequal to a predetermined number of user-generated mouse motions withrespect to the annotation interface.
 14. The system of claim 13, whereinthe segmentation correction machine learning model is a convolutionalneural network and the effort measurement is used to set one or moretraining weights used by the convolutional neural network.